Using an Aural Classifier to Discriminate Cetacean Vocalizations

View/Open

Date

Author

Metadata

Abstract

To positively identify marine mammals using passive acoustics, large volumes of data are often collected that need to be processed by a trained analyst. To reduce acoustic analyst workload, an automatic detector can be implemented that produces many detections, which feed into an automatic classifier to significantly reduce the number of false detections. This requires the development of a robust classifier capable of performing inter-species classification as well as discriminating cetacean vocalizations from anthropogenic noise sources. A prototype aural classifier was developed at Defence Research and Development Canada that uses perceptual signal features which model the features employed by the human auditory system. The dataset included anthropogenic passive transients and vocalizations from five cetacean species: bowhead, humpback, North Atlantic right, minke and sperm whales. Discriminant analysis was implemented to replace principal component analysis; the projection obtained using discriminant analysis improved between-species discrimination during multiclass cetacean classification, compared to principal component analysis. The aural classifier was able to successfully identify the vocalizing cetacean species. The area under the receiver operating characteristic curve (AUC) is used to quantify the two-class classifier performance and the M-measure is used when there are three or more classes; the maximum possible value of both AUC and M is 1.00 – which is indicative of an ideal classifier model. Accurate classification results were obtained for multiclass classification of all species in the dataset (M = 0.99), and the challenging bowhead/ humpback (AUC = 0.97) and sperm whale click/anthropogenic transient (AUC = 1.00) two-class classifications.